Urbanization advances at unprecedented rates, resulting in negative effects
on the environment and human well-being. Remote sensing has the potential to
mitigate these effects by supporting sustainable development strategies with
accurate information on urban growth. Deep learning-based methods have achieved
promising urban change detection results from optical satellite image pairs
using convolutional neural networks (ConvNets), transformers, and a multi-task
learning setup. However, transformers have not been leveraged for urban change
detection with multi-temporal data, i.e., >2 images, and multi-task learning
methods lack integration approaches that combine change and segmentation
outputs. To fill this research gap, we propose a continuous urban change
detection method that identifies changes in each consecutive image pair of a
satellite image time series. Specifically, we propose a temporal feature
refinement (TFR) module that utilizes self-attention to improve ConvNet-based
multi-temporal building representations. Furthermore, we propose a multi-task
integration (MTI) module that utilizes Markov networks to find an optimal
building map time series based on segmentation and dense change outputs. The
proposed method effectively identifies urban changes based on high-resolution
satellite image time series acquired by the PlanetScope constellation (F1 score
0.551) and Gaofen-2 (F1 score 0.440). Moreover, our experiments on two
challenging datasets demonstrate the effectiveness of the proposed method
compared to bi-temporal and multi-temporal urban change detection and
segmentation methods.

该研究提出了一种基于深度学习和遥感技术的城市变化检测方法，采用连续多时相卫星图像，结合自注意力和分割技术，有效地识别了城市变化，与其他方法相比表现更加出色。